{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:44:53Z","timestamp":1775144693702,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National natural science foundation of China","doi-asserted-by":"publisher","award":["42077242"],"award-info":[{"award-number":["42077242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR","award":["KF-2019-04-080"],"award-info":[{"award-number":["KF-2019-04-080"]}]},{"name":"Scientific research project of the 13th five-year plan of Jilin province education department","award":["JJKH20200999KJ"],"award-info":[{"award-number":["JJKH20200999KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.<\/jats:p>","DOI":"10.3390\/rs13030440","type":"journal-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T12:20:26Z","timestamp":1611750026000},"page":"440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Haiming","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2806-858X","authenticated-orcid":false,"given":"Mingchang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China"}]},{"given":"Fengyan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Guodong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Junqian","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Siqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"},{"name":"Xi\u2019an Center of Mineral Resources Survey, China Geological Survey, Xi\u2019an 710100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"ref_1","first-page":"105","article-title":"Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Stars"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, S.P., Shen, Y.Y., Lu, M., and Zhang, Y.J. (2019). Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sens., 11.","DOI":"10.3390\/rs11111343"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, C., Zhang, H., Zhang, B., and Wu, F. (2019). Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network. Remote Sens., 11.","DOI":"10.3390\/rs11091091"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shirowzhan, S., Sepasgozar, S.M.E., Li, H., Trinder, J., and Tang, P.B. (2019). Comparative analysis of machine learning and point-based algorithms for detecting 3D changes in buildings over time using bi-temporal lidar data. Automat. Constr., 105.","DOI":"10.1016\/j.autcon.2019.102841"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1109\/TGRS.2019.2956756","article-title":"Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery","volume":"57","author":"Mou","year":"2019","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, X., Cao, Y.X., and Li, J.Y. (2020). An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images. Remote Sens. Environ., 244.","DOI":"10.1016\/j.rse.2020.111802"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Du, S.J., Zhang, Y.S., Qin, R.J., Yang, Z.H., Zou, Z.R., Tang, Y.Q., and Fan, C. (2016). Building Change Detection Using Old Aerial Images and New LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8121030"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2014Approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TGRS.2016.2627638","article-title":"Cosegmentation for Object-Based Building Change Detection From High-Resolution Remotely Sensed Images","volume":"55","author":"Xiao","year":"2017","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shi, W.Z., Zhang, M., Zhang, R., Chen, S.X., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Hasanlou, M., and Amani, M. (2020). A New End-to-End Multi-Dimensional CNN Framework for Land Cover\/Land Use Change Detection in Multi-Source Remote Sensing Datasets. Remote Sens., 12.","DOI":"10.3390\/rs12122010"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","article-title":"Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering","volume":"21","author":"Gong","year":"2012","journal-title":"IEEE T Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain","volume":"45","author":"Bovolo","year":"2007","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2011.2171493","article-title":"A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images","volume":"50","author":"Bovolo","year":"2012","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_17","first-page":"131","article-title":"Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data","volume":"50","author":"Thonfeld","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1080\/01431161.2017.1395966","article-title":"Novel class-relativity non-local means with principal component analysis for multitemporal SAR image change detection","volume":"39","author":"Jia","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TGRS.2013.2266673","article-title":"Slow Feature Analysis for Change Detection in Multispectral Imagery","volume":"52","author":"Wu","year":"2014","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_20","first-page":"4676","article-title":"Unsupervised Scene Change Detection via Latent Dirichlet Allocation and Multivariate Alteration Detection","volume":"11","author":"Du","year":"2018","journal-title":"IEEE J. Stars"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TGRS.2017.2650198","article-title":"Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images","volume":"55","author":"Gong","year":"2017","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1109\/TGRS.2011.2168534","article-title":"Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images with Active-Learning-Based Compound Classification","volume":"50","author":"Demir","year":"2012","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1109\/36.602528","article-title":"An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images","volume":"35","author":"Bruzzone","year":"1997","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1016\/j.patrec.2004.06.002","article-title":"Detection of land-cover transitions by combining multidate classifiers","volume":"25","author":"Bruzzone","year":"2004","journal-title":"Pattern Recogn. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/36.763299","article-title":"A neural-statistical approach to multitemporal and multisource remote-sensing image classification","volume":"37","author":"Bruzzone","year":"1999","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sinha, P., Kumar, L., and Reid, N. (2016). Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection. Remote Sens., 8.","DOI":"10.3390\/rs8020107"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pang, S.Y., Hu, X.Y., Cai, Z.L., Gong, J.Q., and Zhang, M. (2018). Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images. Sensors, 18.","DOI":"10.3390\/s18040966"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2820","DOI":"10.1109\/TGRS.2006.879498","article-title":"Change detection of multitemporal SAR data in urban areas combining feature-based and pixel-based techniques","volume":"44","author":"Gamba","year":"2006","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1109\/TGRS.2014.2363548","article-title":"Building Change Detection in Multitemporal Very High Resolution SAR Images","volume":"53","author":"Marin","year":"2015","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_30","first-page":"519","article-title":"A Building Extraction Method via Graph Cuts Algorithm by Fusion of LiDAR Point Cloud and Orthoimage","volume":"47","author":"Du","year":"2018","journal-title":"Acta Geodaetica et Cartographica Sinica"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ball, J.E., Anderson, D.T., and Chan, C.S. (2017). Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.042609"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Perconti, P., and Plebe, A. (2020). Deep learning and cognitive science. Cognition, 203.","DOI":"10.1016\/j.cognition.2020.104365"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","article-title":"Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks","volume":"27","author":"Gong","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lyu, H.B., Lu, H., and Mou, L.C. (2016). Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060506"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, C., Liu, X.L., and Liao, M.S. (2018). A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification. Remote Sens., 10.","DOI":"10.3390\/rs10020342"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Y.Y., Wang, C., and Zhang, H. (2017, January 19\u201321). Integrating H-A-alpha with Fully Convolutional Networks for Fully PolSAR Classification. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (Rsip 2017), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958799"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Song, A., Choi, J., Han, Y., and Kim, Y. (2018). Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks. Remote Sens., 10.","DOI":"10.3390\/rs10111827"},{"key":"ref_42","first-page":"483","article-title":"A novel approach to change detection in SAR images with CNN classification(Article)","volume":"6","author":"Xu","year":"2017","journal-title":"J. Radars"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, M.C., Zhang, H.M., Sun, W.W., Li, S., Wang, F.Y., and Yang, G.D. (2020). A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12121933"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, D.Z., Li, P., and Lv, P. (2020). Change Detection of Remote Sensing Images Based on Attention Mechanism. Comput. Intel. Neurosci., 2020.","DOI":"10.1155\/2020\/6430627"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, P., Wei, Y.M., Wang, Q.J., Chen, Y., and Xie, J.J. (2020). Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model. Remote Sens., 12.","DOI":"10.3390\/rs12050894"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/j.procs.2019.09.318","article-title":"Contour-Aware Residual W-Net for Nuclei Segmentation","volume":"159","author":"Das","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gargiulo, M., Dell\u2019Aglio, D.A.G., Iodice, A., Riccio, D., and Ruello, G. (2020). Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net. Sensors, 20.","DOI":"10.3390\/s20102969"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","article-title":"From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques","volume":"58","author":"Hou","year":"2020","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"78909","DOI":"10.1109\/ACCESS.2019.2922839","article-title":"High-Resolution Remote Sensing Image Change Detection Combined With Pixel-Level and Object-Level","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cao, C., Liu, X., Yang, Y., Yu, Y., Wang, J., Wang, Z., Huang, Y., Wang, L., Huang, C., and Xu, W. (2015, January 11\u201318). Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks(Conference Paper). Proceedings of the IEEE International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.338"},{"key":"ref_56","first-page":"2204","article-title":"Recurrent Models of Visual Attention","volume":"27","author":"Mnih","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhang, X., Niu, X., Wang, F., and Zhang, X. (2019). Scene Classification of High-Resolution Remotely Sensed Image Based on ResNet. J. Geovis. Spatial Anal., 3.","DOI":"10.1007\/s41651-019-0039-9"},{"key":"ref_58","unstructured":"Zhao, J. (2015). Image Feature Extraction and Semantic Analysis, Chongqing University Press."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Solorzano, J.V., Gallardo-Cruz, J.A., Gonzalez, E.J., Peralta-Carreta, C., Hernandez-Gomez, M., de Oca, A.F., and Cervantes-Jimenez, L.G. (2018). Contrasting the potential of Fourier transformed ordination and gray level co-occurrence matrix textures to model a tropical swamp forest\u2019s structural and diversity attributes. J. Appl. Remote Sens., 12.","DOI":"10.1117\/1.JRS.12.036006"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8424","DOI":"10.3390\/rs6098424","article-title":"A Multichannel Gray Level Co-Occurrence Matrix for Multi\/Hyperspectral Image Texture Representation","volume":"6","author":"Huang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, R.X., Li, X.H., and Li, J. (2018). Object-Based Features for House Detection from RGB High-Resolution Images. Remote Sens., 10.","DOI":"10.3390\/rs10030451"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yi, Y.N., Zhang, Z.J., Zhang, W.C., Zhang, C.R., Li, W.D., and Zhao, T. (2019). Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11151774"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/widm.1125","article-title":"Support vector machines in engineering: An overview","volume":"4","year":"2014","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_64","first-page":"113","article-title":"Very High Resolution Remote Sensing Imagery Classification Using a Fusion of Random Forest and Deep Learning Technique-Subtropical Area for Example","volume":"13","author":"Dong","year":"2020","journal-title":"IEEE J. Stars"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TNNLS.2018.2832648","article-title":"A Cost-Sensitive Deep Belief Network for Imbalanced Classification","volume":"30","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1007\/s10489-018-1379-8","article-title":"Multimodal correlation deep belief networks for multi-view classification","volume":"49","author":"Zhang","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.3788\/OPE.20192712.2722","article-title":"Semantic segmentation based on DeepLabV3+ and superpixel optimization","volume":"27","author":"Ren","year":"2019","journal-title":"Opt. Precis. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set","volume":"57","author":"Ji","year":"2019","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, W.M., Qi, J.B., Wan, P., Wang, H.T., Xie, D.H., Wang, X.Y., and Yan, G.J. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/440\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:16:03Z","timestamp":1760159763000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,27]]},"references-count":70,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030440"],"URL":"https:\/\/doi.org\/10.3390\/rs13030440","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,27]]}}}